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Jackson, R., & Ahlborn, G. (1990). The role of protected areas in Nepal in maintaining viable populations of snow leopards. Int.Ped.Book of Snow Leopards, 6, 51–69.
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Blomqvist, L. (1995). Three decades of Snow Leopards Panthera uncia in Captivity. Int.Zoo Yearbook, 34, 178–185.
Abstract: The author reports the status of the captive population of snow leopards over the last three decades. Genetic and demographic information is also provided. The captive population as of 1992 was 541 leopards. klf. I
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Poyarkov, A. D., Munkhtsog, B., Korablev, M. P., Kuksin, A. N., Alexandrov, D. Y., Chistopolova, M. D., Hernandez-Blanco, J. A., Munkhtogtokh, O., Karnaukhov, A. S., Lkhamsuren, N., Bayaraa, M., Jackson, R. M., Maheshwari, A., Rozhnov, V. V. (2020). Assurance of the existence of a trans-boundary population of the snow leopard (Panthera uncia) at Tsagaanshuvuut – Tsagan- Shibetu SPA at the Mongolia-Russia border. Integrative Zoology, (15), 224–231.
Abstract: The existence of a trans-boundary population of the snow leopard (Panthera uncia) that inhabits the massifs of Tsagaanshuvuut (Mongolia) – Tsagan-Shibetu (Russia) was determined through non-invasive genetic analysis of scat samples and by studying the structure of territory use by a collared female individual. The genetic analysis included species identification of samples through sequencing of a fragment of the cytochrome b gene and individual identification using a panel of 8 microsatellites. The home range of a female snow leopard marked with a satellite Global Positioning System (GPS) collar was represented by the minimum convex polygon method (MCP) 100, the MCP 95 method and the fixed kernel 95 method. The results revealed insignificant genetic differentiation between snow leopards that inhabit both massifs (minimal fixation index [FST]), and the data testify to the unity of the cross-border group. Moreover, 5 common individuals were identified from Mongolian and Russian territories. This finding clearly shows that their home range includes territories of both countries. In addition, regular movement of a collared snow leopard in Mongolia and Russia confirmed the existence of a cross-border snow leopard group. These data support that trans-boundary conservation is important for snow leopards in both countries. We conclude that it is crucial for Russia to study the northern range of snow leopards in Asia.
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Blomqvist, L. (2003). The global snow leopard population in captivity 2001 (Vol. 8).
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Blomqvist, L. (1980). The 1979 world register for the captive population of snow leopards, Panthera uncia. In L. Blomqvist (Ed.), International Pedigree Book of Snow Leopards (pp. 62–75). Helsinki: Helsinki Zoo.
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Blomqvist, L. (1982). The 1981 annual report of the captive snow leopards (Panthera uncia) population. International Pedigree Book of Snow Leopards, 3.
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Freeman, H. (1980). The snow leopard, today and yesterday. In L. Blomqvist (Ed.), International Pedigree Book of Snow Leopards, Vol. 2 (Vol. 2, pp. 37–43). Helsinki: Helsinki Zoo.
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Blomqvist, L. (1979). The 1978 register for the captive population of snow leopards, Panthera uncia. International Zoo News, 26(7-8), 17–23.
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Khanyari, M., Zhumabai uulu, K., Luecke, S., Mishra, C.,
Suryawanshi, K. (2020). Understanding population baselines: status of mountain ungulate
populations in the Central Tien Shan Mountains, Kyrgyzstan. Mammalia, , 1–8.
Abstract: We assessed the density of argali (Ovis ammon) and ibex
(Capra sibirica) in Sarychat-Ertash Nature Reserve and its neighbouring
Koiluu valley. Sarychat is a protected area, while Koiluu is a human-use
landscape which is a partly licenced hunting concession for mountain
ungulates and has several livestock herders and their permanent
residential structures. Population monitoring of mountain ungulates can
help in setting measurable conservation targets such as appropriate
trophy hunting quotas and to assess habitat suitability for predators
like snow leopards (Panthera uncia). We employed the double-observer
method to survey 573 km2 of mountain ungulate habitat inside Sarychat
and 407 km2 inside Koiluu. The estimated densities of ibex and argali in
Sarychat were 2.26 (95% CI 1.47–3.52) individuals km-2 and 1.54 (95% CI
1.01–2.20) individuals km-2, respectively. Total ungulate density in
Sarychat was 3.80 (95% CI 2.47–5.72) individuals km-2. We did not record
argali in Koiluu, whereas the density of ibex was 0.75 (95% CI
0.50–1.27) individuals km-2. While strictly protected areas can achieve
high densities of mountain ungulates, multi-use areas can harbour
meaningful
though suppressed populations. Conservation of mountain ungulates and
their predators can be enhanced by maintaining Sarychat-like “pristine”
areas interspersed within a matrix of multi-use areas like Koiluu.
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Durbach, I., Borchers, D., Sutherland, C., Sharma, K. (2020). Fast, flexible alternatives to regular grid designs for spatial
capture–recapture..
Abstract: Spatial capture–recapture (SCR) methods use the location of
detectors (camera traps, hair snares and live-capture traps) and the
locations at which animals were detected (their spatial capture
histories) to estimate animal density. Despite the often large expense
and effort involved in placing detectors in a landscape, there has been
relatively little work on how detectors should be located. A natural
criterion is to place traps so as to maximize the precision of density
estimators, but the lack of a closed-form expression for precision has
made optimizing this criterion computationally demanding. 2. Recent
results by Efford and Boulanger (2019) show that precision can be well
approximated by a function of the expected number of detected
individuals and expected number of recapture events, both of which can
be evaluated at low computational cost. We use these results to develop
a method for obtaining survey designs that optimize this approximate
precision for SCR studies using count or binary proximity detectors, or
multi-catch traps. 3. We show how the basic design protocol can be
extended to incorporate spatially varying distributions of activity
centres and animal detectability. We illustrate our approach by
simulating from a camera trap study of snow leopards in Mongolia and
comparing estimates from our designs to those generated by regular or
optimized grid designs. Optimizing detector placement increased the
number of detected individuals and recaptures, but this did not always
lead to more precise density estimators due to less precise estimation
of the effective sampling area. In most cases, the precision of density
estimators was comparable to that obtained with grid designs, with
improvement in some scenarios where approximate CV(¬D) < 20% and density
varied spatially. 4. Designs generated using our approach are
transparent and statistically grounded. They can be produced for survey
regions of any shape, adapt to known information about animal density
and detectability, and are potentially easier and less costly to
implement. We recommend their use as good, flexible candidate designs
for SCR surveys when reasonable knowledge of model parameters exists. We
provide software for researchers to construct their own designs, in the
form of updates to design functions in the r package oSCR.
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